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Minimum Feature Selection for Epileptic Seizure Classification using Wavelet-based Feature Extraction and a Fuzzy Neural Network
2014
Applied Mathematics & Information Sciences
This paper proposes a method that uses a wavelet transform (WT) and a fuzzy neural network to select the minimum number of features for classifying normal signals and epileptic seizure signals from the electroencephalogram (EEG) signals of people with epileptic symptoms and those of healthy people. WT was used to select the minimum number of features by creating detail coefficients and approximation coefficients from EEG signals. 40 initial features were obtained from the created wavelet
doi:10.12785/amis/080344
fatcat:uagaqa6xubhczd257qh3quc3a4